摘要
对密集场景下用户网络感知问题进行研究,提出了一种密集场景下网络负荷预测和4G/5G带宽自适应的新方法。通过Apriori算法对各类大数据进行学习建模,得到小区不同时段的业务模型,再利用均值漂移算法,建立不同带宽下用户数的业务模型,输出不同时段小区感知需求最低带宽配置,同步实现4G/5G小区带宽的自适应优化。验证结果表明,4G/5G网络负荷得到缓解,且用户关键质量指标(Key Quality Index,KQI)得到提升。
The problem of user network perception in dense scenarios is studied to propose a new method of network load forecasting and 4G/5G bandwidth adaptation in dense scenarios.The Apriori algorithm is adopted to learn and model all kinds of big data to obtain the service model of the cell in different periods.The mean shift algorithm is used to establish the service model of the number of users under different bandwidth,and to output the minimum bandwidth configuration of the cell perceived demand in different periods,and synchronously realize the adaptive optimization of 4G/5G cell bandwidth.The verification results show that the 4G/5G network load is alleviated,and the key performance indicator(KQI)is improved.
作者
贾磊
张璐岩
JIA Lei;ZHANG Luyan(Hanzhong Branch,China Mobile Communications Group Shaanxi Co.,LTD.,Hanzhong 723099,China;Network Optimization Center,China Mobile Communication Group Shaanxi Co.,LTD.,Xi'an 710077,China)
出处
《西安邮电大学学报》
2021年第4期20-25,共6页
Journal of Xi’an University of Posts and Telecommunications
作者简介
贾磊(1980-),男,硕士,高级工程师,从事4G/5G无线网优化、大数据分析应用研究。E-mail:jialei@sn.chinamobile.com;张璐岩(1988-),男,硕士,高级工程师,从事移动通信系统、大数据分析应用。E-mail:zhangluyan@sn.chinamobile.com。
